Technical Field
The present disclosure relates to fire detection systems and methods.
Description of the Related Art
Fire detection based on image processing find applications in a variety of fields in computer vision area, due to the growing interests on video surveillance and image understanding and classification.
Wildfire and indoor/close range fire monitoring remains the typical field of application due to the advantages of a video detection systems with respect to the alarm systems based on optical or ion sensors, which suffer several drawbacks. For example the alarm is triggered only when the fire is close to the sensors, which are sensitive to temperature, humidity and other atmospheric variables; moreover they are unable to provide any additional information such as the fire location, and size and degree of burning. They imply also the need to verify the existence of a fire by visiting the location and their cost is high.
Video-based fire monitoring systems, on the contrary, allow monitoring wide areas and have a faster response without the need of moving to the location to confirm the presence of fire. They provide also direct information about the size and the intensity of the flames and their cost is reduced because cameras that are already installed in many public places for surveillance purposes can be exploited.
Automatic or semiautomatic fire detection by image analysis presents also several difficulties. For example, the light conditions may affect the efficiency of the system: reflection shadows, daily and night light may make difficult to discern flames in a not controlled environment. Moreover the scene can includes a variety of moving, fire colored objects and the low-cost camera sensor often provides poor resolution images. Such elements can yield false alarms.
Known fire detection algorithms employ color as the basic feature to detect fire in a video sequence. Examples of such type of technique is described in:                Bo-Ho Cho, Jong-Wook Bae, Sung-Hwan Jung, Image Processing-based Fire Detection System Using Statistic Color Model, International Conference on Advanced Language Processing and Web Information Technology, 2008.        T. Celik, H. Demirel, H. Ozkaramanli, M. Uyguroglu, Fire Detection Using Statistical Color Model In Video Sequences, Journal of Visual Communication & Image Representation, Vol. 18, pp. 176-185, 2007.        
It is known to perform a background removal step as described in document “Y. Benezeth, P.M. Jodoin, B. Emile, H. Laurent, C. Rosenberger, Comparative Study of Background Subtraction Algorithms, Journal of Electronic Imaging, Volume 19, Issue 3, DOI 10.1117/1.3456695, pp. 1-30, 2010”.
Fire detection algorithms employing Fuzzy Logic are also known. Document “A. Del Amo, J. Montero, D. Gomez, Fuzzy Logic Applications to Fire Control Systems, IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, Jul. 16-21, 2006” describes a method performing a segmentation of the image in classes discerning the natural homogeneous areas from fire zones.
In document “Byoung Chul Ko, SunJae Ham, Jae-Yeal Nam, Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire Flames, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21, No. 12, pp. 1903-1912, Dec. 2011”a Fuzzy Finite Automata (FFA) with probability density functions based on visual features is defined to handling the continuous irregular pattern that characterizes flames, is disclosed.
One more approach is presented in “K. Angayarkkani, N. Radhakrishnan, Efficient Forest Fire Detection System: A Spatial Data Mining and Image Processing Based Approach, International Journal of Computer Science and Network Security, Vol. 9, n. 3, Mar. 2009”. This document focuses on the problem of forest monitoring and defines a fuzzy rule base from the spatial data with the presence of fires. The digital images from the spatial data are converted to YCbCr color space and then segmented by employing anisotropic diffusion to identify fire regions.
With reference to the fuzzy logic theory, document E. H. Mamdani and S. Assilian, An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1-13, 1975, describes the so called Mamdani inference process.